Explainable AI for Sharp Injury Detection via Transfer Learning

This study presents an explainable artificial intelligence (XAI) approach for the identification of sharp injuries using transfer learning with pre-trained deep neural networks. Leveraging models such as ResNet, VGG, or Inception, the system enhances classification accuracy while ensuring model transparency through interpretability techniques like Grad-CAM and LIME. The framework aims to assist medical and forensic experts in making informed, reliable decisions by combining high-performance image recognition with visual explanations.

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